Systems and methods for disease control and management

ABSTRACT

A system for creating and updating one or more models for providing treatment recommendations for health conditions may receive weight measurements from a weight measuring device, execute a basic model based on information regarding the health condition, to generate treatment recommendations, generate reminders prompting the user to obtain weight measurements, estimate an impact of the user data on the weight measurements, generate a modified model, based on the basic model, the received weight measurements, the received user data, and the estimated impact of the user data, the modified model including personalized treatment recommendations specific to the user, and transmit the reminders, the treatment recommendations, and the personalized treatment recommendations to the user device.

RELATED APPLICATION(S)

This application is a continuation in part of U.S. application Ser. No.15/783,674, filed on Oct. 13, 2017, which is a continuation of U.S.application Ser. No. 14/315,053, filed on Jun. 25, 2014, now U.S. Pat.No. 9,824,190, which claims the benefit of priority to U.S. ProvisionalApplication No. 61/839,528, filed on Jun. 26, 2013. This application isalso a continuation of U.S. application Ser. No. 16/848,310, filed onApr. 14, 2020, which is a continuation in part of U.S. application Ser.No. 16/179,060, filed on Nov. 2, 2018, which is a continuation of U.S.application Ser. No. 14/714,828, filed on May 18, 2015, now U.S. Pat.No. 10,152,675, which is a continuation of U.S. application Ser. No.14/462,033, filed on Aug. 18, 2014, now U.S. Pat. No. 9,064,040, whichis a continuation of U.S. application Ser. No. 13/428,763, filed on Mar.23, 2012, now U.S. Pat. No. 8,838,513, which claims the benefit ofpriority to U.S. Provisional Application No. 61/467,131, filed on Mar.24, 2011, all of which are incorporated herein by reference, in theirentireties. U.S. application Ser. No. 16/848,310 is also a continuationof U.S. application Ser. No. 16/186,996, filed on Nov. 12, 2018, whichis a continuation of U.S. application Ser. No. 15/670,551, filed on Aug.7, 2017 (now abandoned), which is a continuation of U.S. applicationSer. No. 12/071,486, filed on Feb. 21, 2008, now U.S. Pat. No.9,754,077, which claims benefit to U.S. Provisional Application No.60/902,490, filed on Feb. 22, 2007, all of which are incorporated hereinby reference, in their entireties.

TECHNICAL FIELD

The present invention generally relates to the field of diseasemanagement. More particularly, and without limitations, the inventionrelates to systems and methods for disease management by providingtreatment recommendations and information based on relevant datacollected about a patient.

BACKGROUND

Generally speaking, disease management refers to a wide range ofactivities that may affect a patient's health status. These activitiesinclude programs to improve a patient's compliance with their treatmentregimen, and helping the patient adjust their regimen according toevidence-based treatment guidelines. The goal of these programs is toattempt to maintain or improve the patient's health status and qualityof life, and to reduce the total costs of health care.

One approach is to identify and provide a variety of appropriateservices to patients that are suitable for a certain program. Examplesof such services include periodic phone calls to a patient to monitorthe status of the patient, personalized goal-oriented feedback on thepatient's self care, access to nurse call centers, and educationalmaterials. Some of these programs attempt to modify services based ondata obtained from the patient's self reports and administrative claims.These programs attempt to help patients identify regimens, manage theirsymptoms, self-monitor their conditions, and comply with theirtreatment. The drawbacks of this type of approach include the enormousresources necessary to provide these human-based services, and the lackof timely, appropriate, and accurate patient data to provide appropriateservices. For example, a single case manager can only assist a limitednumber of patients. As a result, such a case manager may, for example,be able to contact a patient only once a month and may be forced to relyon out-dated data for the patient.

An alternative approach for disease management is to use a system thatprovides corrective action to a patient based on limited informationprovided by the patient. For example, a diabetes patient can providetheir current glucose value using an electronic patient-operatedapparatus, and the system can recommend a corrective action to thepatient based on the application of formulas well known in the art. Acorrective action can be either administration of an extra insulin doseor consumption of carbohydrates. A drawback of this approach is that itfails to provide customized disease management for each individualpatient. Similarly, the formulas that can be applied for any patientpart of the general population do not take into account the completedynamics of a particular's patient's disease and the corrective actionsadvised to a patient are limited. As a result, a patient is denied theoptimal treatment recommendations that can only be targeted for aparticular patient. Finally, the success of the system is whollydependent on a proactive and conscientious patient.

In view of the foregoing, there is a need for an improved solution fordisease management. In particular, there is a need for systems andmethods for effective disease management that can developrecommendations for a patient based on the particular patient's data.

SUMMARY

The present invention provides methods, computer-readable media, andsystems for providing disease management. This is achieved by providingtreatment recommendations targeted for a particular patient based on thepatient's data.

In one exemplary embodiment, a system is provided including, forexample, an input device for receiving patient data; a transmitter fortransmitting the received patient data from the input device; and adisease management server. The disease management server may include areceiver for receiving the transmitted patient data, a processor fordeveloping a treatment recommendation, and an output device foroutputting the treatment recommendation. The processor may developtreatment recommendations by executing a basic model of thephysiological system of the patient to generate a modified model for thepatient based on the patient data, performing a statistical analysis ofthe patient data to detect data excursions of the parameter values,using the modified model to determine factors causing the dataexcursions, and using the model to develop a treatment recommendation toameliorate negative effects of the disease. The patient data may includebut is not limited to the following parameters: patient carbohydrateintake, patient medication doses, blood glucose level, activity of thepatient, and the corresponding time and optional context for the data.

The processor in the disease management server of the exemplary systemmay generate a plurality of treatment recommendations, including, forexample, recommendations for dosing and timing of medication, and forpatient dietary behavior. Executing the model comprises applying analgorithm to the patient data in a current state in conjunction withauxiliary patient data including, for example, patient blood lipidlevel, blood pressure, age, gender, height and weight, and race. Theprocessor may be further configured to develop recommendations forcollection of the patient data to improve the model, wherein thecollection recommendations comprise recommendations for timing of bloodglucose measurement. The processor may be further configured to performstatistical analysis employing a statistical design of experimentsmethodology and multivariate analyses to identify the factors causingthe data excursions.

In another embodiment, the treatment recommendations may be developed atpredetermined intervals, which may be determined based on a most-recentpatient data collection time. The treatment recommendations may includea recommended time and dose of medication, including recommendedcombinations of available medications. The treatment recommendations mayalso include a recommended time and amount of carbohydrate intake.

In another alternate embodiment, the input device may be apatient-interactive interface operated by the patient. The system mayinclude a portable device configured to operate the patient-interactiveinterface and the transmitter, wherein the transmitter transmits signalsover a wireless communication to the disease management server. Thesystem may also include a physician interface operable to receiveinformation about the patient from the disease management server.

In another alternate embodiment, the patient data may be calibrated at aplurality of reading points to provide more information onpharmacokinetic/pharmacodynamic interactions of the patient.

In another alternate embodiment, generating the modified model mayinclude utilizing equations that simulate interaction of primary factorseffecting rate of change of the blood glucose of the patient, whereinthe primary factors are calculated based on the patient data. Theprimary factors may include rate of digestion of the patientcarbohydrate intake, cellular uptake of blood glucose by the patient,and impact of glucose inventory in liver, skeletal muscle, and fattissues of the patient. The equation for the cellular uptake is afunction of the blood glucose level, level of insulin acting as acatalyst, resistance factors, and digestive-activated hormones, andwherein the level of insulin is calculated based on the patientmedication doses, patient pancreatic production, and insulin kinetics.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate embodiments and aspects of thepresent invention. In the drawings:

FIG. 1 illustrates an exemplary system, consistent with an embodiment ofthe invention.

FIG. 2 is a flowchart of an exemplary method, consistent with anembodiment of the present invention;

FIG. 3 is a flowchart of an exemplary method for collecting patientdata, consistent with an embodiment of the present invention;

FIG. 4 depicts an example of a disease managements server developingtreatment recommendations, consistent with an embodiment of the presentinvention;

FIG. 5 is an exemplary diagram showing a generation of a modified modelfor a patient, consistent with an embodiment of the present invention;

FIG. 6 is an exemplary diagram showing a determination of the insulin ofa patient, consistent with an embodiment of the present invention;

DESCRIPTION OF THE EMBODIMENTS

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several exemplary embodiments and features of the invention aredescribed herein, modifications, adaptations and other implementationsare possible, without departing from the spirit and scope of theinvention. For example, substitutions, additions, or modifications maybe made to the components illustrated in the drawings, and the exemplarymethods described herein may be modified by substituting, reordering, oradding steps to the disclosed methods. Accordingly, the followingdetailed description does not limit the invention. Instead, the properscope of the invention is defined by the appended claims.

FIG. 1 illustrates components of an exemplary system 100, consistentwith an embodiment of the present invention. The two main components ofthe system are an input device 110 and a disease management server 130.A transmitter 120 may be a separate component or part of the inputdevice. Transmitter 120 may include a modem to transmit informationcollected by input device 110 to disease management server 130.Transmitter 120 may also be used to receive information from diseasemanagement server 130 to provide to the user through the input device110. For example, disease management server 130 may send informationregarding what data it needs input device 100 collect from the patient.

Input device 110 and the transmitter 120 may be part of a singlecomputer system. Alternatively, the transmitter may be on anintermediary computer system between the input device 110 and thedisease management server 130. The transmitter 120 may serve as theintermediary for communication between the input device 110 and thedisease management server. The components 110, 120, 130 may communicatewith each over a communication medium 125. The communication medium maybe any desired type of communication channel, including (but not limitedto) the public switched telephone network (VSTN), cellular channel,other wireless channel, internal computer bus, intranet, internet, etc.

The input device 110 may be used to collect variety of patient data in avariety of ways. The input device 110 may be connected to one or morebiometric measurement devices to collect biometric data. The biometricmeasurement devices may be implanted in the patient, connected to thepatient, and/or completely external. A biometric measurement device maybe part of or connected to the input device 110, or it may be astand-alone device. A stand-alone biometric measurement device maycommunicate wirelessly with the input device 110. Examples of biometricmeasurement devices include, but are not limited to, scales, externalblood pressure monitors, cardiac data sensors, implantable blood glucosesensors for continuous sensing, external blood glucose monitors, etc.

The input device 110 may also receive information from devices foradministering medication. For example, the input device 110 may beconnected to an insulin-delivery device. The input device 110 mayreceive information detailing the insulin doses a patient has taken fromthe insulin-delivery device and the times of dose administration.

The input device 110 may also receive information from other ancillarydevices to collect a variety of information about the patient's behaviorand status. For example, the input device 110 may be connected to apedometer worn by the patient or exercise equipment used by the patientto track the physical activity of the patient. The input device 110 maybe connected to a GPS system to track the movement and location of thepatient. The input device may also be connected to a device that keepstrack of a patient's dietary behavior.

The input device 110 may also receive information by having the patiententer particular information. The disease management server 130 may sendrequest for particular information to be entered by the patient usingthe input device 110. The input device 110 may prompt the user to enterthe particular information. The request sent by the disease managementserver 130 may be in the form of surveys designed to collect particularinformation. The surveys may be targeted to collect information about apatient's dietary behavior (e.g., carbohydrate intake, portion control),self-monitoring of blood glucose, medication doses, physical activity,psychological status, alcohol intake, smoking activity, and any otherinformation that might effect the patient's well being. The input device110 may prompt the patient to enter information by displaying the surveyand receiving the patient's responses to the questions posed in thesurvey. The scripts may also be designed to educate the patient or topromote positive behavior/behavior modification by the patient.

The transmitter 120 may send the patient data collected by the inputdevice 110 to the disease management server 130 immediately after thedata is entered into the input device 110 by the patient.

As shown in FIG. 1, disease management server 130 may include a receiver131, a processor 132, and an output device 133. The receiver 131 mayreceive patient data from the transmitter 120. The processor 132 mayexecute a program for generating/maintaining a modified model for apatient and developing treatment recommendations for the patient usingthe model based on the patient data. The output device 133 may outputinformation to either the patient, a third party health care provider,or a remote device based on the treatment recommendations developed bythe processor 132.

The processor 132 may generate a modified model for the patient based onthe patient data the disease management server 130 receives through thereceiver 131. The modified model may be used to estimate impact of achange in the patient's regimen (e.g., medication, food intake, physicalactivity) on the physiological system of the patient. The processor maygenerate a modified model for the patient by executing a basic model. Abasic model models a physiological system of a patient based onestablished knowledge of disease dynamics and factors. The patient datais used to generate the modified model for a particular patient. Themodified model can provide guidance to improve the patient's regimen andto gain a better understanding of how a particular patient is beingaffected by a disease dynamic. The use of patient data over an extendedperiod of time allows the modification and improvements of the modifiedmodel as more data is collected for a particular patient. The modifiedmodel may also be used even when limited/incomplete patient data isreceived.

After the generation of a modified model, the processor 132 may performa statistical analysis of patient data to detect data excursions. A dataexcursion is an event in which a value of a parameter (e.g., bloodglucose level) is outside the normal/expected range for the parameter.The processor 132 may use the modified model it generated to determinethe factors causing the data excursions and the effects of such dataexcursions. The modified model may then be used to develop a pluralityof treatment recommendations to ameliorate negative effects of thedisease. The output device 133 within the disease management server 130may output information based on the treatment recommendations to thepatient, a device relate to the patient, or a third party.

FIG. 2 is a flowchart of an exemplary method 200, consistent with anembodiment of the present invention. Method 200 may be executed on asystem illustrated in FIG. 1. According to the method, at step 201, theinput device 110 may collect patient data. At step 202, the transmitter120 may transmit the patient data to the disease management server 130.At step 203, the disease management server 130 may generate a modifiedmodel for the patient. At step 204 the disease management server 130 maydevelop treatment recommendations for the patient using the modifiedmodel generated at step 203. At step 205 the disease management server130 may deploy treatment recommendations developed at step 204, by usingthe output device 133.

FIG. 3 is a flowchart of another exemplary method 300, consistent withan embodiment of the present invention. Method 300 depicts an example ofan input device 110 receiving patient data. Steps 301, 303, 304, 305 ofthis method may be performed in no particular order. Steps 301, 303,304, 305 present a limited set of examples of the different patient datathat can be collected and how it may be collected by an input device110.

At step 301, the input device 110 may receive data collected by one ormore biometric measurement devices. The input device 110 may receive thedata collected by one or more biometric measurement devices every time ameasurement occurs by the biometric measurement device, at a certainpredetermined interval, or when the input device is prompted to collectthe biometric measurement data. Alternatively, as discussed above, atstep 301, the biometric measurement data may be entered by the patientinto the input device 110.

At step 302, the input device may prompt the user to enter specificinformation pertaining to factors that can effect the well being of thepatient. The patient may be prompted to enter such information by beingasked a series of questions. The patient may receive reminders throughthe input device to enter certain type of information at a certain time.Step 302 may repeat several times to collect different types of datafrom the patient at steps 301, 303, 304, 305.

At step 303, the input device 110 may receive information about thepatient's dietary behavior. This information may be simply entered bythe patient on a regular basis using the input device. The patient mayuse a remote workstation to enter the information and have ittransferred to the input device. The information may be entered inresponse to the prompt(s) at step 302. The information may be receivedfrom a device that can track a patient's dietary behavior. An example ofsuch a device is a food/drink caloric intake calculator. The calculatormay be implemented, for example, on a cell phone, and a patient mayenter information regarding his/her dietary behavior into the cellphone. Alternatively, the calculator may be part of the input device.The information may also be received by the input device 110 from arestaurant where the patient eats. Another example is a camera-basedfood calculator. The input device 110 may be able to receive pictures offood eaten by the patient. Image recognition techniques may later beused to identify the food consumed by the patient, and determine thecarbohydrate intake of the patient for example.

At step 304, the input device 110 may receive information about whatmedications the patient is taking, at what doses, and at what times. Thepatient may simply enter the information to be received by the inputdevice independently, or the patient may enter the information inresponse to a prompt at step 302. The input device 110 may also beconnected to other devices, and may receive information regarding thepatient's medication intake from the other devices.

At step 305, the input device 110 may receive any other pertinentinformation from the patient that may assist successful diseasemanagement. This information may be received directly from the patient,a health care provider, a monitoring device, a third party, etc. Aperson can be prompted by the input device 110 to enter the information.Examples of information that may be collected may relate to thepatient's exercise, smoking, alcohol intake, etc.

The input device 110 may also receive information from the patient atsteps 301, 303, 304, and 305 regarding patients attitude towardsmodifying behavior that affects the respective data received at eachstep.

FIG. 4 is a flowchart of another exemplary method 400, consistent withan embodiment of the present invention. Method 400 depicts an example ofa disease management server 130 generating treatment recommendations fora patient. At step 401, a receiver 131 of the disease management server130 receives patient data from a transmitter 120. The patent data iscollected by input device 110 (see FIG. 3) and transmitted bytransmitter 120 from the input device 110 to the disease managementserver 130. Steps 402, 403, 404, 405 may be performed by a processor 132in the disease management server.

At step 402, the disease management server 130 uses the patient data togenerate a modified model of the patient's physiological system. Themodified model may be used to estimate the impact of a change of acertain factor (data) on the patient (other data). The modified modelrepresents the effects of a change in different data on the particularpatient's physiological system, and in turn how the physiological systemof the patient effects the data. The modified model may be a previouslygenerated modified model updated based on new patient data. The model isgenerated by executing a basic model based on the patient data (402).The basic model represents the minimal assumptions and parameters of aperson's physiological system and the correlation of certain factors.How the factors interact and correlate is different for differentpatients. The patient data is used to adjust the basic model andgenerate a modified model for a particular patient. The more patientdata is collected over time, the more accurate the modified model is forthe particular patient. In addition, the collection of data frommultiple patients may be used to improve the basic model.

At step 403, the disease management server 130 performs a statisticalanalysis to detect data excursions in the patient data. A data excursionmay be indicated when certain data collected at a certain time isoutside the normal range that can be expected for that data for theparticular patient. For example, a data excursion may be a blood glucosemeasurement that is outside the normal blood glucose range for theparticular patient.

At step 404, the disease management server 130 uses the modified modelgenerated at step 402 to determine the factors causing the dataexcursions detected at step 403. For example, the disease managementserver 130 may determine if a change in amount of timing of medicationor food intake of the patient caused the data excursion of blood glucosemeasurement.

At step 405, the disease management server 130 may develop treatmentrecommendations to ameliorate negative effects of the patient's disease.The disease management server 130 may use the modified model todetermine the treatment recommendations which are optimal for thepatient. For example, if a data excursion caused a negative effect onthe patient, a treatment recommendation may be developed to prevent sucha data excursion in the future. Examples of treatment recommendationsmay include adjusting food or medication intake, adjusting how and whatdata is collected, advising the patient to modify certain behaviors,providing educational materials to the patient, alerting the patient'shealth care provider, etc.

At step 406, the output device 133 of disease management server 130 mayoutput the treatment recommendations developed at step 405. Thetreatment recommendations are transmitted to the appropriate party ordevice. For example, a treatment recommendation to increase thepatient's insulin dose can be transmitted directly to theinsulin-delivery device (pump). If, for example, the treatmentrecommendation is to collect certain additional data about the patientto provide optimal treatment recommendations in the future, a script canbe transmitted to the patient through the input device 110 to collectthe additional data at certain times.

FIG. 5 is an exemplary diagram showing a generation of a modified modelfor a patient, consistent with an embodiment of the present invention.The modified model is generated by the basic model. The diagram, ineffect, is representing the basic model. The model in this embodimentfocuses on the primary factors that impact the daily blood glucose (BC)of a patient: carbohydrate intake, insulin level of the patient, andglucose inventory of the patient. The model inherently includes withinthese primary factors the complex interactions of the hormonal-signalingnetwork that supports the endocrine processes. First, it is necessary todetermine the carbohydrate intake 501, insulin level 503, and glucoseinventory 505 of the patient to generate the model. The patient data isused to determine the respective primary factors. The carbohydrateintake is determined based on the amount of carbohydrates ingested bythe patient that become the primary source of blood glucose for thepatient (501). The insulin level of the patient is determined on avariety of factors including dosing, pancreatic production, and kinetics(505, see FIG. 6 for further detail). The glucose inventory isdetermined based on the glucose stored in the liver, skeletal muscle,and fat tissues of the patient (506).

After determining the carbohydrate intake of the patient, an impact ofthe carbohydrate intake (carbs) on concentration of the blood glucosemay be modeled (502). This may be modeled based on the followingequation for instantaneous blood glucose concentration:

${\frac{d\lbrack{BG}\rbrack}{dt} = {\beta\frac{d\left\lbrack {{carbs},{BG},{incretins}} \right\rbrack}{dt}}},$wherein [BG] may be blood glucose (BG) concentration, [carbs] may berepresented as a first order kinetics function of time (t), β may be forexample about 5 BG units/carbohydrate unit, and incretins are thedigestive-activated hormones of the patient.

The impact of insulin on transfer of blood glucose into patient's cells(504) is accounted for in the modified model. Insulin is accounted foras a catalyst, not as a compound that forms with blood glucose. Theinstantaneous rate decrease of blood glucose concentration frominstantaneous insulin concentration in blood may be modeled based on thefollowing equation:

${{- \frac{d\lbrack{BG}\rbrack}{dt}} = {{\alpha(t)} \times {\lbrack{BG}\rbrack\lbrack{insulin}\rbrack}}},$wherein [insulin] may be the instantaneous insulin concentration inblood, α includes resistance impact of diabetes and is about 0.3 if rateis −50 BG units/insulin unit above BG of 140, and α(t) includes diurnalvariations. Alternatively, α(t) may be approximated without any timedependence, using the 24 hour mean.

Finally, it is necessary to account for the impact of the glucoseinventory (IG) on the blood glucose (BG) (506), where the glucoseinventory absorbs blood glucose on storage and release blood glucose onretrieval (mass action kinetics). This may be modeled based on thefollowing equation:

${\frac{d\lbrack{BG}\rbrack}{dt} = {- \frac{d\lbrack{IG}\rbrack}{dt}}},$where

${\frac{d\lbrack{IG}\rbrack}{dt} - {\gamma\left( {\lbrack{IG}\rbrack + \frac{\left\{ {{- \lbrack{BG}\rbrack} - \lbrack{IG}\rbrack} \right\}}{\left\{ {1 + {\exp\left( {\omega\left\{ {\tau - {\alpha\lbrack{insulin}\rbrack}} \right\}} \right)}} \right\}}} \right)}},$wherein r is the threshold; if resistant insulin is greater then thethreshold, then blood glucose decreases by going into liver and fat asglucose storage (IG) and is not released through the liver. And whereinγ relates concentration of glucose to changes in IG. The IGfunctionality may be modeled by other similar forms at the conceptuallevel since the phenomenological description is inclomplete.

The total change in blood glucose over a time interval (508) may bemodeled based on the sum of the three factors 502, 504, 506 describedabove. The complete basic model may be based on the following equation(509):

${{\frac{d}{dt}{{BG}(t)}} = \left\lbrack {\left\lbrack {{\beta\left( {\frac{d}{dt}{{carbs}(t)}} \right)} - {\alpha \cdot {{BG}(t)} \cdot {{insulin}(t)}}} \right\rbrack - {\frac{d}{dt}{{IG}(t)}} + {errors}} \right\rbrack},$where the storage or release of blood glucose as mediated by resistantinsulin may be expressed in the following equation:

${{\frac{d}{dt}{{IG}(t)}} = {- {\gamma\left\lbrack {{{IG}(t)} + \frac{\left( {{- {{BG}(t)}} - {{IG}(t)}} \right)}{\left\lbrack {1 + {\exp\left\lbrack {W \cdot \left( {\tau - {B \cdot {{insulin}(t)}}} \right\rbrack} \right\rbrack}} \right.}} \right\rbrack}}},$wherein W is the switching rate (i.e., the rate that IG switches fromintake mode to output mode of BG). Errors may account for any additionalor unknown factors besides the primary factors (507). Statisticalmethods may be used to estimate model parameters and their expectedvariations for a patient.

The model can then be used to determine factors causing the dataexcursions 404. The relative contributions of the primary factors mayvary over the 24 hour day, especially adjacent to meals and fastingevents. For example, the fasting segments of the BG profile betweenmeals would see little impact from the carbohydrates (502), and, if inaddition, insulin levels are near their norm's as defined by its storagethreshold, then insulin (504) becomes the dominant effect. Hence, thisallows an estimation of the a parameter and the insulin profile of thepatient based on the model. The dosing profile is known (503), andcomparison with the estimated profile provides information on pancreaticactivity. Under these conditions, one can also test if a changes amongdifferent fasting events. The testing can be accomplished through atreatment recommendation. The model provide a guide for timely regimenoptimization for the particular patient. The optimization is based on aminimal-assumption model (MAM) that fits the major features in the datapatterns of the patient. The MAM for BG data patterns may be expressed,for example, as follows: BG=MAM(t, injections, orals, digestion,diurnals, activity, liver processes, immune reaction, betaislets)+error.

Given α, one can estimate inventory parameters from the segments of themodel capturing fasting events at non-normal levels of insulin. Withthese two sources established, one can now estimate the impact ofcarbohydrates during meals, since carbohydrates kinetics are known.Similarly, the MAM may be used to determine the effect on the patient'sBG of changing other factors (ex. medications). A key feature of MAM isthat it can model answers to “what if” questions for each individualpatient given any scenario of lifestyle and treatment regimen/conditionsover any interval of time both retrospectively and prospectively.

FIG. 6 is a diagram showing a determination of the insulin of a patient(503), consistent with an embodiment of the present invention. Theinsulin of a patient may be calculated 604 based on the insulin from themedication received by the patient 601, the insulin produced by thepatient's pancreatic beta cells 602, and mass action kinetics of insulin603. The effect of mass action kinetics on insulin may be accounted for,based on the following formula: insulin(t)=D (dbeta [0.3(t−0.5), 2.1,7]+E), wherein D is the units of insulin dose received by the patient,and wherein E is the approximate equilibrium level of 24 hour SAinsulin. Optionally, one may use kinetics models to create the insulindosing profiles. The insulin profile may take into account thedifference between long-acting and short-acting kinetics that vary basedon the source of the insulin.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and does not limit the invention tothe precise forms or embodiments disclosed. Modifications andadaptations of the invention will be apparent to those skilled in theart from consideration of the specification and practice of thedisclosed embodiments of the invention. For example, the describedimplementations include software, but systems and methods consistentwith the present invention may be implemented as a combination ofhardware and software or in hardware alone. Examples of hardware includecomputing or processing systems, including personal computers, servers,laptops, mainframes, micro-processors and the like. Additionally,although aspects of the invention are described for being stored inmemory, one skilled in the art will appreciate that these aspects canalso be stored on other types of computer-readable media, such assecondary storage devices, for example, hard disks, floppy disks, orCD-ROM, the Internet or other propagation medium, or other forms of RAMor ROM.

Computer programs based on the written description and methods of thisinvention are within the skill of an experienced developer. The variousprograms or program modules can be created using any of the techniquesknown to one skilled in the art or can be designed in connection withexisting software. For example, program sections or program modules canbe designed in or by means of Java, C++, HTML, XML, or HTML withincluded Java applets. One or more of such software sections or modulescan be integrated into a computer system or existing e-mail or browsersoftware.

Moreover, while illustrative embodiments of the invention have beendescribed herein, the scope of the invention includes any and allembodiments having equivalent elements, modifications, omissions,combinations (e.g., of aspects across various embodiments), adaptationsand/or alterations as would be appreciated by those in the art based onthe present disclosure. The limitations in the claims are to beinterpreted broadly based on the language employed in the claims and notlimited to examples described in the present specification or during theprosecution of the application, which examples are to be construed asnon-exclusive. Further, the steps of the disclosed methods may bemodified in any manner, including by reordering steps and/or insertingor deleting steps, without departing from the principles of theinvention. It is intended, therefore, that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims and their fullscope of equivalents.

What is claimed is:
 1. A system for creating and updating one or moremodels for providing treatment recommendations for health conditions,comprising: a weight measuring device configured to obtain weightmeasurements and to wirelessly transmit the obtained weightmeasurements; and a disease management server in wireless communicationwith the weight measuring device, the disease management serverincluding: a receiver for receiving the weight measurements transmittedfrom the weight measuring device, and configured to receive user datafrom a user device; and one or more processors configured to: execute abasic model of a physiological system generic to any user to generate amodified model specific to a physiological system of the user, whereingenerating the modified model comprises updating the basic model basedon information specific to the user; perform a statistical analysis todetect a data excursion in the received weight measurements, wherein adata excursion is detected when a weight measurement, of the receivedweight measurements, collected at a particular time, is outside of apredetermined range; execute the modified model to generate personalizednotifications, based on the modified model, wherein executing themodified model comprises determining one or more factors causing adetected data excursion, and developing the personalized notificationsbased on the determined one or more factors that caused a detected dataexcursion, the personalized notifications including adjusting a timingof obtaining weight measurements; generate an updated modified model, atleast once, based on the modified model, wherein generating the updatedmodified model comprises training the modified model with the determinedone or more factors learned from the received weight measurements toupdate an ability of the modified model, in the updated modified model,to estimate an impact of changes in the one or more factors on thephysiological system of the user; perform the statistical analysis todetect a data excursion in received additional weight measurements,wherein a data excursion in the received additional weight measurementsis detected when a weight measurement, of the received additional weightmeasurements, collected at a particular time, is outside of thepredetermined range; execute the updated modified model to generateupdated personalized notifications, wherein executing the updatedmodified model comprises determining one or more factors causing adetected data excursion in the received additional weight measurements,and developing the updated personalized notifications based on thedetermined one or more factors that caused a detected data excursion inthe received additional weight measurements, the updated personalizednotifications including adjusting a timing of obtaining weightmeasurements; and output the personalized notifications and the updatedpersonalized notifications to at least one of the weight measuringdevice and the user device, wherein each of the weight measuring deviceand the user device is configured to receive and to output thepersonalized notifications and the updated personalized notifications tothe user.
 2. The system according to claim 1, wherein the receiverreceives the weight measurements every time the weight measuring devicetransmits a weight measurement to the disease management server.
 3. Thesystem according to claim 1, wherein the receiver receives the weightmeasurements at predetermined intervals.
 4. The system according toclaim 1, wherein the user data includes a food and drink caloric intakeand exercise data of the user.
 5. The system according to claim 1,wherein the personalized notifications and the updated personalizednotifications further include one or more of advice to modify behaviorand educational materials.
 6. A computer-implemented method for creatingand updating one or more models for providing personalized notificationsfor health conditions, the method comprising: receiving, using areceiver of a disease management server, weight measurements from aweight measuring device wirelessly connected to the disease managementserver, and user data of the user, from a user device; executing, usingone or more processors of the disease management server, a basic modelof a physiological system generic to any user to generate a modifiedmodel specific to a physiological system of the user, wherein generatingthe modified model comprises updating the basic model based oninformation specific to the user; performing, using the one or moreprocessors, a statistical analysis to detect a data excursion in thereceived weight measurements, wherein a data excursion is detected whena weight measurement, of the received weight measurements, collected ata particular time, is outside of a predetermined range; executing, usingthe one or more processors, the modified model to generate personalizednotifications, based on the modified model, wherein executing themodified model comprises determining one or more factors causing adetected data excursion, and developing the personalized notificationsbased on the determined one or more factors that caused a detected dataexcursion, the personalized notifications including adjusting a timingof obtaining weight measurements; generating, using the one or moreprocessors, an updated modified model, at least once, based on themodified model, wherein generating the updated modified model comprisestraining the modified model with the determined one or more factorslearned from the received weight measurements to update an ability ofthe modified model, in the updated modified model, to estimate an impactof changes in the one or more factors on the physiological system of theuser; performing, using the one or more processors, the statisticalanalysis to detect a data excursion in received additional weightmeasurements, wherein a data excursion in the received additional weightmeasurements is detected when a weight measurement, of the receivedadditional weight measurements, collected at a particular time, isoutside of the predetermined range; executing the updated modified modelto generate updated personalized notifications, wherein executing theupdated modified model comprises determining one or more factors causinga detected data excursion in the received additional weightmeasurements, and developing the updated personalized notificationsbased on the determined one or more factors that caused a detected dataexcursion in the received additional weight measurements, the updatedpersonalized notifications including adjusting a timing of obtainingweight measurements; and outputting the personalized notifications andthe updated personalized notifications to at least one of the weightmeasuring device and the user device; and outputting, using the weightmeasuring device or the user device, the personalized notifications andthe updated personalized notifications to the user.
 7. The methodaccording to claim 6, wherein the weight measurements are received everytime the weight measuring device transmits a weight measurement to thedisease management server.
 8. The method according to claim 6, whereinthe weight measurements are received at predetermined intervals.
 9. Themethod according to claim 6 wherein the user data includes a food anddrink caloric intake and exercise data of the user.
 10. A system forcreating and updating one or more modified models for providingpersonalized notifications for health conditions, comprising: a glucosemeasuring device configured to obtain glucose measurements from a user,and to wirelessly transmit the obtained glucose measurements; a weightmeasuring device configured to obtain weight measurements from the user,and to wirelessly transmit the obtained weight measurements; and adisease management server in wireless communication with the glucosemeasuring device and the weight measuring device, the disease managementserver comprising: a receiver for receiving the glucose measurements,the weight measurements, and user data, from a user device; and one ormore processors configured to: execute a basic model of a physiologicalsystem generic to any user to generate one or more modified modelsspecific to a physiological system of the user, wherein generating theone or more modified models comprises updating the basic model based oninformation specific to the user; perform a statistical analysis todetect a data excursion in at least one of the received glucosemeasurements and the received weight measurements, wherein a dataexcursion is detected when a glucose measurement, of the receivedglucose measurements, collected at a particular time, is outside of apredetermined range of glucose measurements, or a weight measurement, ofthe received weight measurements, is outside of a predetermined range ofweight measurements; execute the one or more modified models to generatepersonalized notifications, based on the one or more modified models,wherein executing the one or more modified models comprises determiningone or more factors causing a detected data excursion, and developingthe personalized notifications based on the determined one or morefactors that caused a detected data excursion, the personalizedtreatment recommendations including one or more of adjusting a timing ofobtaining the glucose measurements and adjusting a timing of obtainingthe weight measurements; generate one or more updated modified models,based on the one or more modified models, wherein generating the one ormore updated modified models comprises training the one or more modifiedmodels with the determined one or more factors learned from the receivedglucose measurements and the received weight measurements, to update anability of the one or more modified models, in the corresponding one ormore updated modified models, to estimate an impact of changes in theone or more factors on the physiological system of the user; perform thestatistical analysis to detect a data excursion in at least one ofreceived additional glucose measurements and additional weightmeasurements, wherein a data excursion is detected when a glucosemeasurement, of the received additional glucose measurements, collectedat a particular time, is outside of the predetermined range of glucosemeasurements, or a weight measurement, of the received additional weightmeasurements, is outside of the predetermined range of weightmeasurements; execute the one or more updated modified models, togenerate updated personalized notifications, wherein executing the oneor more updated modified models comprises determining one or morefactors causing a detected data excursion in the at least one of thereceived additional glucose measurements and the additional weightmeasurements, and developing the updated personalized notificationsbased on the determined one or more factors that caused a detected dataexcursion in the received additional glucose measurements or theadditional weight measurements, the updated personalized notificationsincluding at least one of adjusting a timing of obtaining the glucosemeasurements and adjusting a timing of obtaining the weightmeasurements; and output the personalized notifications and the updatedpersonalized notifications to at least one of the glucose measuringdevice, the weight measuring device, and the user device, wherein eachof the glucose measuring device, the weight measuring device, and theuser device is further configured to receive and to output thepersonalized notifications and the updated personalized notifications tothe user.
 11. The system according to claim 10, wherein the receiverreceives a glucose measurement from the glucose measurement device everytime a glucose measurement is obtained and a weight measurement from theweight measuring device every time a weight measurement is obtained. 12.The system according to claim 10, wherein the receiver receives each ofthe obtained glucose measurements and the obtained weight measurementsat predetermined intervals.
 13. The system according to claim 10,wherein the user data includes a food and drink caloric intake andexercise data of the user.
 14. The system according to claim 13, whereinat least one of the personalized notifications and the updatedpersonalized notifications further includes exercise recommendations forthe user.
 15. The system according to claim 13, wherein at least one ofthe personalized notifications and the updated personalizednotifications further includes one or more of advice to modify behavioror educational materials.
 16. The system according to claim 10, whereinthe one or more processors are further configured to output an alert toa provider associated with the user.
 17. The system according to claim16, wherein the one or more processors output an alert to the providerassociated with the user when a measurement value of at least one of thereceived glucose measurements and the received weight measurements isoutside a range of normal measurement values.
 18. The system accordingto claim 10, wherein the glucose measuring device transmits the glucosemeasurements, the weight measuring device transmits the weightmeasurements, and the disease management server transmits thepersonalized notifications and the updated personalized notificationsvia one of a cellular channel or a wireless network.
 19. The systemaccording to claim 10, wherein the glucose measuring device is acontinuous glucose monitor.
 20. The system according to claim 10,wherein at least one of the personalized notifications and the updatedpersonalized notifications includes adjusting a medication intake.